CONF vijayasenan:ICASSP:2008/IDIAP COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION Vijayasenan, Deepu Valente, Fabio Bourlard, Hervé EXTERNAL http://publications.idiap.ch/attachments/papers/2008/vijayasenan-ICASSP-2008.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/vijayasenan:rr07-51 Related documents IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP) 2008 IDIAP-RR 07-51 This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative Information Bottleneck for very fast diarization. Here we consider the combination of sequential and agglomerative clustering for avoiding local maxima of the objective function and for purification. Experiments are run on the RT06 eval data. Sequential Clustering with oracle model selection can reduce the speaker error by $10\%$ w.r.t. agglomerative clustering. When the model selection is based on Normalized Mutual Information criterion, a relative improvement of $5\%$ is obtained using a combination of agglomerative and sequential clustering. REPORT vijayasenan:rr07-51/IDIAP COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION Vijayasenan, Deepu Valente, Fabio Bourlard, Hervé EXTERNAL http://publications.idiap.ch/attachments/reports/2007/vijayasenan-idiap-rr-07-51.pdf PUBLIC Idiap-RR-51-2007 2007 IDIAP This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative Information Bottleneck for very fast diarization. Here we consider the combination of sequential and agglomerative clustering for avoiding local maxima of the objective function and for purification. Experiments are run on the RT06 eval data. Sequential Clustering with oracle model selection can reduce the speaker error by $10\%$ w.r.t. agglomerative clustering. When the model selection is based on Normalized Mutual Information criterion, a relative improvement of $5\%$ is obtained using a combination of agglomerative and sequential clustering.